SCOT: Multi-Source Cross-City Transfer with Optimal-Transport Soft-Correspondence Objective
Cross-city transfer improves prediction in label-scarce cities by leveraging labeled data from other cities, but it becomes challenging when cities adopt incompatible partitions and no ground-truth region correspondences exist. Existing approaches either rely on heuristic region matching, which is often sensitive to anchor choices, or perform distribution-level alignment that leaves correspondences implicit and can be unstable under strong heterogeneity. We propose SCOT, a cross-city representation learning framework that learns explicit soft correspondences between unequal region sets via Sinkhorn-based entropic optimal transport. SCOT further sharpens transferable structure with an OT-w...
| Authors | Yuyao Wang, Min Yang, Meng Chen et al. |
|---|---|
| Primary category | cs.LG |
| Published | 8 April 2026 |
| Last updated at source | 8 May 2026 |
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